Recent generations of empirical research have emphasized the role of cognitive (cf. Dijkstra, Krammer, & van Merrienboer, 1992; Lewalter, 2003) as well as metacognitive strategies in computer-assisted learning (Simons & De Jong, 1992; van den Boom, Paas, van Merrienboer, & van Gog, 2004; Veenman, 1993). Especially the ability of self-monitoring has been shown to be a major moderating variable in self-directed learning with technology (e.g., Herrington & Oliver, 1999; Lan, 1996). However, there still is a need in development as well as application of methods to analyze and improve self-monitoring in individual as well as collaborative computer-assisted learning (CAL). Within this special issue, we combine methods from analyzing self-monitoring within individual and collaborative CAL showing foundational similarities as well as diversities in application. Three articles in this special issue contribute to analysis and fostering of individual CAL focusing on metacognition. The article provided by Elmar Stahl, Stephanie Pieschl, and Rainer Bromme addresses the influence of epistemological beliefs on metacognitive calibration during hypermedia learning. The effect of different epistemological instructions and beliefs on metacognitive calibration and learning outcomes is examined. Therefore, authors investigate in their study if learners are able to differentiate between tasks of different complexity, perform task and goal analyzes, and adapt their learning progress behavior to these self-monitoring outcomes. Furthermore, the role of epistemological beliefs on this self-directed, adaptive process is analyzed. Stahl et al. were able to show that there is a significant relationship between task